Our analysis process. What data we use, how we score it, and why peer benchmarking produces better recommendations than gut feel — whether for a Premier League club or an F1 mid-grid team.
We aggregate commercial data from public filings, club websites, FA registration records, schema.org markup, social APIs, and broadcast rights databases. For each club, we track: shirt sponsor name and estimated deal value, sleeve and training kit partners, digital rights holders, stadium naming rights, and academy partnerships.
We calculate a 0–100 Gambling Exposure Score for each club based on four weighted factors. The score is recalculated monthly as deal valuations and contract durations change.
For football clubs, we group each organisation against a peer set of 4–6 comparable clubs based on league position, stadium capacity (±15,000), average tenure, and geographic market. For motorsport teams, we benchmark within constructor tier (e.g. F1 mid-grid vs mid-grid, Formula E front-runners vs front-runners). Commercial gaps are measured relative to the peer median — not absolute values — which controls for structural market differences across leagues and series.
For each replacement sponsor category, we score demographic overlap (age, income, regional distribution), brand affinity signals (social listening, survey data), and competitive exclusivity windows. The result is a ranked list of the 10 best-fit replacement brands for each club — with estimated deal values, key contacts, and activation model recommendations.
The output is a structured PDF combining exposure score, peer benchmark position, replacement opportunity value, and a prioritised 6-month action plan. Reports are calibrated for commercial directors — not analysts. Every recommendation includes a "why now" rationale tied to market timing.
Try the interactive demo with Riverside FC, or get a real 24-hour intelligence report for any European football club or motorsport team.